Astrophysics: AI shines a new light on exoplanets | Science & Technology

When distant exoplanets pass in front of their star, they block a small portion of the starlight, while an even smaller portion penetrates the planetary atmosphere. This interaction leads to variations in the light spectrum, which mirror the properties of the atmosphere such as chemical composition, temperature, and cloud cover. To be able to analyze these measured spectra, however, scientists require models that are capable of calculating millions of synthetic spectra in a short time. Only by subsequently comparing the calculated spectra with the measured ones do we obtain information about the atmospheric composition of the observed exoplanets. And what is more, the highly detailed new observations coming from the James Webb Space Telescope (JWST) necessitate equally detailed and complex atmospheric models.

Rapid solving of complex equations thanks to AI

A key aspect of exoplanet research is the light scattering in the atmosphere, particularly the scattering off clouds. Previous models were unable to satisfactorily capture this scattering, which led to inaccuracies in the spectral analysis. Physics-informed neural networks offer a decisive advantage here, as they are capable of efficiently solving complex equations. In the just-published study, the researchers trained two such networks. The first model, which was developed without…